Non-Fungible Tokens (NFTs) are a relatively new concept and have been making headlines for the related events happening in the space.

The best way to gauge the sentiments and get basic level stats is to use data from social media. Twitter is a powerful platform for people to express their opinions on any given topic. The tweets which include hashtags(#) related to NFTs are collected.

This dataset can possibly help to capture the trend of NFTs by using available data and answerquestions that help understand how far NFTs have come.

Consumers from Asia and Oceania were more interested in NFTs than their counterparts from Europe, Latin America, or MENA. This is according to information that was compiled from Google Trends, looking at how often people from around the world were searching for the keyword “NFT ” Charts to measure whether an NFT hype was still present or not in 2022 started to appear as consumer data was not always available. One global consumer survey on NFT adoption per country, for example, was held in September 2021—or half a year before the figures shown here. Either way, figures from both 2021 and 2022 suggest that consumers in the United States, the UK, and Germany were initially less interested in NFTs than those from other countries in the world. Why is that? NFT interest tied to high-profile announcements Many heard about NFTs for the first time when news outlets reported the high-value sale of Beeple crypto art piece “The First 5,000 Days” by auction house Christie’s. From that moment on, early adoption became a motivator for several U.S. consumers to acquire NFTs. The main motivation behind buying NFTs in the U.S. by that time, however, was return on investment. The rebranding of Facebook into Meta—by introducing the metaverse, a further application of NFTs, to a large audience—likely caused awareness to grow further. NFT-related announcements did not stop there: Canada’s increase in NFT searches, for example, might be caused by the news of KPMG Canada buying a World of Women NFT in March 2022. Celebrity promotions on Bored Ape Yacht Club (BAYC) in early 2022, while sparking outrage from some, may also have added to the growing familiarity of what an NFT could be. GameFi and the early interest in (Southeast) Asia Several countries at the top of this ranking are found in Asia. Many believe this is due to the rise of blockchain games and the growing number of GameFi users—especially in this region. Take, for instance, the Philippines: the country hit the news in the summer of 2021 as it decided to make cryptocurrencies earned within the NFT game Axie Infinity—originally from Vietnam—taxable. This caused sales made within Axie Infinity one of the world's most valuable NFT collections, to decline, as many believed the game to be an environment where you could earn money by just playing the game.

Mutant Ape Yacht Club NFT Images

NFT image dataset with Mutant Ape Yacht Club artwork and metadata

By huggingnft (From Huggingface) [source]

About this dataset

The NFT Images Dataset for Unconditional Generation, specifically focused on the Mutant Ape Yacht Club, offers valuable information and resources for artificial intelligence and machine learning enthusiasts. This dataset provides comprehensive details about NFT artwork, including high-quality images and associated metadata.

The dataset includes columns such as image and image_original_url, providing direct access to the image files in their original form through valid web addresses. These image files represent unique pieces of digital artwork from the Mutant Ape Yacht Club collection.

Moreover, this dataset offers crucial insights into each NFT artwork through the token_metadata column. This metadata encompasses various essential details regarding the specific piece of art, such as artist information, detailed descriptions, and unique attributes associated with each NFT. These attributes help differentiate one piece of art from another in terms of style, theme, rarity factors, or any other distinctive characteristics.

By utilizing this comprehensive dataset's resources in various AI applications like unconditional generation models or machine learning algorithms, users gain access to a wide range of digital artworks for research or creative purposes. Additionally, with accurate token metadata available alongside each image file, users can explore diverse aspects that contribute to the essence of these digital creations.

How to use the dataset

Introduction:

• Dataset Overview:

  • The dataset contains information about NFT images from the Mutant Ape Yacht Club, including image URLs, IDs, token metadata, and original image URLs.
  • Each artwork in the dataset is represented by an image file and its corresponding metadata.

• Accessing the Dataset:

  • To access this dataset, download or import the provided train.csv file.

• Understanding Key Columns:

  • image (Image file): This column contains the image file of each artwork in the NFT collection.
  • token_metadata (Text): This column includes metadata associated with each artwork such as artist details, description, and attributes.
  • image_original_url (URL): Provides the original URL of each image file in case you need to refer back to it or access additional information.

• Potential Use Cases:

  • Unconditional Generation: The dataset can be used for unconditional generation tasks like training generative models or running experiments on creating novel artworks based on existing ones.

• Preprocessing Steps: Before using this dataset for unconditional generation tasks, consider performing some preprocessing steps such as:

  a) Image Processing: Resize or normalize images for consistent input dimensions if required by your model architecture.

  b) Text Processing: Clean token_metadata column if needed by removing special characters or irrelevant text that may hinder model training.

• Exploratory Data Analysis (EDA): Conducting EDA provides insights into patterns within the database that might help you understand art concepts better or optimize your unconditional generation models. Some possible EDA tasks include:

  a) Image Visualization: Display a subset of images to get a visual understanding of the artworks.

  b) Metadata Analysis: Analyze the distribution or correlation between different attributes mentioned in the token_metadata column.

• Training Unconditional Generation Models: Use this dataset to train generative models such as Variational Autoencoders (VAE), Generative Adversarial Networks (GANs), or other deep learning architectures for creative artwork generation.

• Iterative Model Improvements: Experiment with different model architectures, hyperparameters, and loss functions to enhance the

Research Ideas

• Unconditional Image Generation: This dataset can be used for training generative models to create new and unique NFT artwork. By training a model on this dataset, it can learn the patterns, styles, and attributes of Mutant Ape Yacht Club NFTs and generate new images in a similar style.
• Artistic Style Transfer: The token_metadata associated with each NFT artwork can provide valuable information about the artist, description, and attributes of the image. This metadata can be used for artistic style transfer techniques to apply the unique style of Mutant Ape Yacht Club artworks to other im...

Relatively few Americans - roughly *** out of 10 - invested in NFTs in early 2021, although most investors were found among millennial men. Indeed, around ** percent for millennials indicated in an online survey held in ********** they were currently invested in NFTs. This survey followed only a few weeks after NFTs made Beeple's First 5000 Days became the world's most expensive NFT sale after it was auctioned at Christie's. Note, however, that the source makes it clear these figures concerns investors, not collectors - hinting that ownership among this particular group of people was somewhat different.

NFT DATASET

This dataset consists of 250 collections and their all time statistics such as sales, transactions, ownership and buyers A sample EDA performed by me could be found in the Code section! Do check it out! will update the dataset in every 15 days.

CONTENTS

The dataset is a .csv file with the following columns: - Collections - Sales - Buyers - Transactions - Owners

Scraping code at GitHub repo: https://github.com/hemil26/NFT-Dataset

CREDITS

This dataset has been scraped from https://cryptoslam.io/

INSPIRATION

• Which collection has highest all time buyers
• Is there any correlation between buyers and sales?
• Simple EDA and Visualization
• Amount of ETH earned by a collection?

Dataset Overview Canonical ERC-721 NFT collection reference with deterministic tracing at the row level. One row per deployed NFT contract, providing audit-grade lineage to the first recognition event and parent/genesis derivations.

Chains and Coverage ETH, BSC, Base, Arbitrum, Unichain, Avalanche, Polygon, Celo, Linea, Optimism (others on request). Full history from chain genesis; reorg-aware real-time ingestion and updates.

Schema List the columns exactly as delivered. • contract_address BYTEA - PK; 20-byte NFT contract address • block_number BIGINT - first block where the token was recognized • block_time TIMESTAMPTZ - UTC timestamp when the block was mined • tx_index INTEGER - tx index for that event • log_index INTEGER - log index for that event • name TEXT - NFT collection name (from ABI call name()) • symbol TEXT - NFT symbol (from ABI call symbol()) • metadata_uri TEXT - optional field for NFT metadata base URI (if applicable) • _tracing_id BYTEA - deterministic row-level hash • _parent_tracing_ids BYTEA[] - hash(es) of immediate parent rows in the derivation graph • _genesis_tracing_ids BYTEA[] - hash(es) of original sources (genesis of the derivation path) • _created_at TIMESTAMPTZ - Record creation timestamp. • _updated_at TIMESTAMPTZ - Record last update timestamp

Notes • Use encode(contract_address,'hex') for hex presentation. • Metadata is obtained deterministically via ABI calls; records are included only when both name and symbol are successfully decoded.

Lineage Every row has a verifiable path back to the originating raw events via the lineage triple and tracing graph: • _tracing_id - this row’s identity • _parent_tracing_ids - immediate sources • _genesis_tracing_ids - original on-chain sources This supports audits and exact reprocessing to source transactions/logs/function calls.

Common Use Cases • Canonical token registry for normalization across DeFi datasets • Symbol, name, decimals lookups for accurate unit scaling in analytics • Cross-chain asset identity resolution • Foundation for NFT, LP token, and vault datasets • Integration layer for pricing engines, wallets, and indexers

Quality • Each row includes a cryptographic hash linking back to raw on-chain events for auditability. • Tick-level resolution for precision. • Reorg-aware ingestion ensuring data integrity. • Complete backfills to chain genesis for consistency.

The Ethereum NFTs dataset

This dataset represents the activity of the Ethereum non-fungible token (NFT) market between the following dates: - Start date: April 1, 2021 - End date: September 25, 2021

These data were collected using Moonstream.to as part of Moonstream's open data efforts.

The dataset is based on on-chain NFT Transfer events as its core. We have also created several derived tables which make the data convenient to analyze.

The NFT market is booming in a way that very few people anticipated at the beginning of this year. This has led to comparisons of today's NFT mania to the 17th century Dutch tulip mania.

We are releasing this dataset so that people may form their own opinions and insights about the NFT market based on the analysis of hard data.

Research using this dataset

The structure of the dataset

The entire dataset is contained in a single SQLite database: nfts.sqlite. This section describes the schema of the database.

Contracts, tokens, and events

A non-fungible token is a digital asset representing a distinct idea or physical object.

On the Ethereum blockchain, these tokens are created using Ethereum smart contracts which represent entire collections of non-fungible tokens. The most famous examples of such contracts are CryptoPunks and CryptoKitties.

Every time an NFT is transferred on the Ethereum blockchain, it emits a Transfer event which is stored on the blockchain. This dataset was constructed by crawling the emitted Transfer events that were emitted in the period of time represented by the dataset.

Core relations

The core relations in this dataset are: - mints - events from the Ethereum blockchain indicating the creation of a new NFT. - transfers - events from the Ethereum blockchain indicating a transfer of ownership of a previously minted NFT.

Both tables have the same schema:

CREATE TABLE mints
  (
    event_id TEXT NOT NULL UNIQUE ON CONFLICT FAIL,
    transaction_hash TEXT,
    block_number INTEGER,
    nft_address TEXT REFERENCES nfts(address),
    token_id TEXT,
    from_address TEXT,
    to_address TEXT,
    transaction_value INTEGER,
    timestamp INTEGER
  );

Columns: - event_id - a unique ID associated with each event, generated when we created the dataset - transaction_hash - the hash of the Ethereum transaction in which we observed the event - block_number - the number of the Ethereum transaction block in which the transaction containing the event was mined - nft_address - the address of the smart contract containing the NFT that the event describes - token_id - the identifier that represents the NFT that the event describes within the context of the smart contract with address nft_address - from_address - the address that owned the NFT before the Transfer event denoted by this row in the relation (it is not the address that initiated the transaction in which the NFT transfer took place) - to_address - the address that owned the NFT after the Transfer event denoted by this row in the relation (it is not the address that was the recipient the transaction in which the NFT transfer took place) - transaction_value - the amount of WEI that were sent with the transaction in which the Transfer event took place - timestamp - the Unix timestamp at which the Ethereum transaction block with number block_number was mined into the Ethereum blockchain

Derived relations

For ease of analysis, we have also included several derived relations in this dataset: - current_market_values - the current (estimated) market value of each NFT, in WEI - current_owners - the current owner of each NFT - nfts - available metadata (from Moonstream) about the NFT contracts represented in the dataset - transfer_statistics_by_address - transfers in and out of every address that was involved in an NFT transfer - transfer_values_quartile_10_distribution_per_address and transfer_values_quantile_25_distribution_per_address - for each 10th or 4th quantile of each NFT collection, these relations give the proportion of the value of the most valuable token in that collection that the quantile represents

NFT Platform Market Outlook

According to our latest research, the global NFT platform market size reached USD 2.4 billion in 2024, reflecting robust expansion driven by surging digital asset adoption and blockchain innovation. The market is set to grow at an impressive CAGR of 28.7% from 2025 to 2033, with the total market value forecasted to reach USD 22.3 billion by 2033. This remarkable trajectory is fueled by the increasing mainstream acceptance of NFTs, rapid technological advancements, and the proliferation of use cases across art, gaming, collectibles, music, sports, and real estate sectors. As per the latest research, the NFT platform market is witnessing a paradigm shift, with both individuals and enterprises leveraging NFT platforms for monetization, digital ownership, and transparent transactions.

The primary growth factor for the NFT platform market is the escalating demand for digital ownership and provenance in the creative economy. Artists, musicians, and content creators are increasingly embracing NFTs to monetize their work, ensuring authenticity and ownership rights through blockchain technology. This trend is amplified by the emergence of decentralized marketplaces, which offer creators direct access to global audiences while bypassing traditional intermediaries. Furthermore, the integration of smart contracts automates royalty payments and enhances trust, making NFT platforms highly attractive for both established and emerging creators. The ability to tokenize and trade unique digital assets is fundamentally transforming the value chain in creative industries, driving sustained growth in the NFT platform market.

Another significant driver is the expanding application of NFTs beyond art and collectibles, particularly in gaming, sports, and real estate. In the gaming industry, NFTs enable true ownership of in-game assets, fostering vibrant secondary markets and new monetization models. Sports franchises and athletes are leveraging NFTs for fan engagement, memorabilia, and ticketing, creating novel revenue streams. The real estate sector is also experimenting with tokenized property ownership, fractionalization, and transparent record-keeping, which enhances liquidity and accessibility. This diversification of NFT applications is attracting a broader user base, including enterprises seeking to innovate customer experiences and engagement strategies, further propelling market growth.

Technological advancements are further catalyzing the NFT platform market. The adoption of scalable and energy-efficient blockchain protocols, such as Ethereum 2.0, Flow, and Polygon, is addressing previous concerns around transaction costs and environmental impact. Enhanced interoperability, improved user interfaces, and robust security features are making NFT platforms more accessible and user-friendly. Additionally, the integration of artificial intelligence and data analytics is enabling personalized recommendations, fraud detection, and dynamic pricing, which are critical for building trust and liquidity in NFT marketplaces. These innovations are not only improving the overall user experience but are also fostering institutional participation and mainstream adoption.

The rise of NFT Mint Mobile App solutions is transforming the way users interact with NFT platforms, providing seamless access to digital assets on-the-go. These mobile applications are designed to cater to the growing demand for convenient and user-friendly interfaces, enabling users to mint, buy, sell, and manage NFTs from their smartphones. As the mobile-first approach gains traction, NFT platforms are prioritizing the development of robust mobile apps that offer secure transactions, real-time notifications, and personalized experiences. The integration of biometric authentication and advanced encryption technologies is enhancing security, while intuitive navigation and responsive design are improving user engagement. The NFT Mint Mobile App is becoming an essential tool for both creators and collectors, facilitating greater accessibility and participation in the NFT ecosystem.

From a regional perspective, North America currently dominates the NFT platform market, driven by a mature blockchain ecosystem, high digital literacy, and strong investment activity. The United States, in particular, is home to leading NFT platforms, creative talent, and venture capi

The NFT art trading platform market is experiencing explosive growth, fueled by increasing digital art adoption, the rise of the metaverse, and the growing appeal of digital ownership. While precise market figures for the historical period (2019-2024) aren't provided, a conservative estimate based on publicly available data from various sources suggests a market size exceeding $1 billion by 2024. The current market is highly dynamic, with a Compound Annual Growth Rate (CAGR) that, considering the rapid technological advancements and market expansion, is likely between 25-35% for the forecast period (2025-2033). Major drivers include the increasing popularity of NFTs as investment assets and collectible items, improved blockchain infrastructure enhancing transaction speed and security, and the growing engagement of artists and collectors within virtual worlds. Key trends include the emergence of fractionalized NFTs, increasing integration with Web3 technologies, and the development of more sophisticated marketplace features. However, market restraints include regulatory uncertainty surrounding digital assets, volatility in cryptocurrency prices directly impacting NFT values, and the ongoing challenge of scaling blockchain technology to accommodate high transaction volumes. The market is segmented by platform type (primary, secondary), art style (digital painting, generative art, 3D models, etc.), and geographical region. Leading companies such as OpenSea, SuperRare, Foundation, Nifty Gateway, and Rarible dominate the space, each vying for market share through distinct features and targeted user bases. The decentralization of these platforms, whilst offering certain advantages, also presents challenges in terms of regulation and consumer protection. The future of the NFT art trading platform market looks promising, but it remains susceptible to market fluctuations and technological disruptions. The integration of advanced technologies such as AI and AR in NFT creation and trading will shape future growth. Furthermore, enhanced user experiences, increased accessibility, and a more robust regulatory framework will be crucial for wider adoption and sustainable growth. The continued expansion of the metaverse and its increasing intertwining with the art world will be significant catalysts for future market expansion. The success of individual platforms hinges upon their ability to attract and retain both artists and collectors, provide a secure and user-friendly platform, and adapt to the ever-evolving landscape of blockchain technology. Strategic partnerships and collaborations will also play a crucial role in driving market expansion and consolidating market leadership.

AI in NFT Market Outlook

According to our latest research, the AI in NFT market size reached USD 1.32 billion globally in 2024, driven by the rapid adoption of artificial intelligence technologies in the creation, management, and trading of non-fungible tokens (NFTs). The market is exhibiting robust momentum, with a projected CAGR of 28.7% from 2025 to 2033. By the end of 2033, the global AI in NFT market is forecasted to attain a value of approximately USD 12.15 billion. This substantial growth is primarily fueled by the increasing convergence of AI and blockchain technology, which is revolutionizing the NFT ecosystem by enabling smarter, more personalized, and highly secure digital assets.

One of the primary growth factors in the AI in NFT market is the significant enhancement of NFT creation and curation processes through artificial intelligence. AI algorithms are now being leveraged to generate unique digital art, music, and collectibles, improving both the quality and diversity of NFTs available in the marketplace. These intelligent systems can analyze vast datasets to identify trends, predict user preferences, and automate the creation of highly sought-after digital assets. As a result, creators and enterprises are increasingly adopting AI-powered tools to streamline workflows, reduce time-to-market, and boost the overall value proposition of NFTs. This technological synergy not only democratizes access to NFT creation but also opens up new revenue streams for artists and developers, thus propelling the market forward.

Another crucial driver for the AI in NFT market is the growing demand for personalized and interactive digital experiences. AI technologies such as machine learning, natural language processing, and computer vision enable NFTs to deliver dynamic content that adapts to user behavior and preferences. For instance, in the gaming and music segments, AI-powered NFTs can offer personalized soundtracks, in-game assets, and interactive art pieces that evolve based on user input or external data feeds. This level of interactivity not only enhances user engagement but also increases the perceived value of NFTs, making them more appealing to collectors and investors. Furthermore, AI-driven analytics provide marketplaces and enterprises with actionable insights into user behavior, facilitating targeted marketing and improved customer retention.

The integration of AI in NFT marketplaces is also addressing longstanding challenges related to security, authenticity, and scalability. AI-based verification tools are being deployed to detect fraudulent activities, authenticate digital assets, and ensure the provenance of NFTs. These advancements are particularly relevant in high-value segments such as art, real estate, and sports memorabilia, where trust and transparency are paramount. Additionally, AI-driven automation is streamlining the management of NFT portfolios, enabling enterprises to efficiently track, value, and trade digital assets across multiple platforms. This comprehensive approach to security and management is fostering greater confidence among institutional investors and large enterprises, thereby accelerating market growth.

From a regional perspective, North America continues to dominate the AI in NFT market, accounting for more than 38% of the global market share in 2024. This leadership is attributed to the region's advanced technological infrastructure, high adoption rates of blockchain and AI solutions, and a vibrant ecosystem of NFT creators and enterprises. Europe and the Asia Pacific region are also witnessing substantial growth, driven by increasing investments in digital art, gaming, and collectibles, as well as supportive regulatory frameworks. Emerging markets in Latin America and Middle East & Africa are gradually catching up, fueled by rising digital literacy and expanding internet penetration. Overall, the global landscape for AI in NFT is becoming increasingly competitive and diverse, with each region contributing unique strengths and opportunities for market expansion.

Component Analysis

The AI in NFT market is segmented by component into software and services, each playing a pivotal role in shaping the ecosystem. Software solutions are at the core of the market, providing the foundational infrastructure

NFT‑Free Digital Collectibles for Loyalty Market Outlook

According to our latest research, the NFT‑Free Digital Collectibles for Loyalty market size reached USD 1.12 billion in 2024 globally, with a robust CAGR of 17.4% expected through 2033. By the end of the forecast period, the market is projected to attain a value of USD 5.35 billion. This significant growth is primarily driven by the rapid adoption of digital loyalty solutions across diverse industries and the increasing preference for frictionless, blockchain-inspired engagement methods that do not require NFT ownership or cryptocurrency knowledge. As per our latest research, brands and enterprises are leveraging NFT-free digital collectibles to foster deeper customer loyalty, enhance engagement, and differentiate themselves in a competitive landscape.

The growth trajectory of the NFT‑Free Digital Collectibles for Loyalty market is underpinned by several compelling factors. Foremost among these is the shift in consumer expectations towards personalized and experiential rewards. Modern consumers, especially millennials and Gen Z, are seeking loyalty programs that offer more than just transactional benefits. NFT-free digital collectibles, which can include branded avatars, badges, and digital memorabilia, provide a unique and interactive way for brands to connect with their audience. This trend is further amplified by the growing penetration of smartphones and digital platforms, which facilitate seamless distribution and redemption of digital collectibles, making them accessible to a broader demographic.

Another critical growth factor is the increasing focus on data privacy and regulatory compliance. Traditional NFT-based solutions often require users to create wallets and interact with cryptocurrencies, raising concerns about privacy, security, and regulatory risks. NFT-free digital collectibles circumvent these challenges by leveraging blockchain-inspired technologies without the complexities of tokenization. This approach appeals to both enterprises and end-users, as it enables the creation of verifiable, unique digital assets while ensuring compliance with data protection regulations such as GDPR and CCPA. Consequently, businesses are more inclined to integrate NFT-free solutions into their loyalty programs, fostering wider adoption and market expansion.

Additionally, the integration of gamification elements and real-time engagement strategies is propelling the market forward. Brands are increasingly embedding NFT-free digital collectibles into gamified loyalty ecosystems, where customers can earn, trade, or unlock exclusive digital items through participation in challenges, events, or social sharing. This not only boosts customer retention and lifetime value but also generates valuable behavioral data that can be used for targeted marketing and personalized offers. The scalability and low entry barriers of NFT-free solutions make them particularly attractive to small and medium enterprises (SMEs), further broadening the market base.

From a regional perspective, North America remains the dominant market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The strong presence of technology-driven retail and entertainment sectors, coupled with high digital literacy rates, is fueling adoption in these regions. Asia Pacific, in particular, is emerging as a high-growth market, driven by rapid urbanization, a burgeoning middle class, and increasing investments in digital transformation initiatives. Meanwhile, Latin America and the Middle East & Africa are witnessing steady growth, supported by rising smartphone penetration and the expansion of digital commerce ecosystems. Regional dynamics are expected to evolve further as global brands localize their loyalty offerings to cater to diverse consumer preferences and regulatory environments.

Type Analysis

The Type segment of the NFT‑Free Digital Collectibles for Loyalty market is categorized into Branded Collectibles, Event-Based Collectibles, Gamified Collectibles, and Others. Branded Collectibles, which include digital assets such as custom avatars, stickers, and exclusive digital art, have emerged as the most popular type in 2024. Brands are leveraging these collectibles to reinforce their identity, drive brand recall, and foster a sense of belonging among their customers. By offering limited-edition digital items tied to marketing campaigns or product launch

From April 2021 to February 2025, the number of sales involving non-fungible tokens (NFTs) in the art segment declined significantly. As of April 15, 2021, roughly ****** NFTs were sold in the art segment during the previous 30 days. While total sales peaked at around ******* as of August 15, 2021, they have experienced an overall decreasing trend since then. As of February 15, 2025, the aggregated number of sales recorded on the Ethereum, Ronin, and Flow blockchains over 30 days was approximately *****. What is the sales value of art and collectibles NFTs? In 2023, the global sales value of art and collectibles NFTs declined sharply over the previous year, with these two segments generating, combined, roughly *** billion U.S. dollars. That year, collectibles were by far the most profitable NFT segment, accounting for over ** percent of total sales. What are the most popular NFT collections? In 2024, CryptoPunks and Bored Ape Yacht Club were the profile picture non-fungible tokens with the highest market cap. These collections, also known as PFP NFTs, refer to those non-fungible tokens that have often been used as profile pictures on social media. When focusing on the art segment, Chromie Squiggle by Snowfro was the NFT art collection with the highest market cap in 2024.

BackgroundThere is little evidence on how best to present diagnostic information to doctors and whether this makes any difference to clinical management. We undertook a randomised controlled trial to see if different data presentations altered clinicians’ decision to further investigate or treat a patient with a fictitious disorder (“Green syndrome”) and their ability to determine post-test probability.MethodsWe recruited doctors registered with the United Kingdom’s largest online network for medical doctors between 10 July and 6” November 2012. Participants were randomised to one of four arms: (a) text summary of sensitivity and specificity, (b) Fagan’s nomogram, (c) probability-modifying plot (PMP), (d) natural frequency tree (NFT). The main outcome measure was the decision whether to treat, not treat or undertake a brain biopsy on the hypothetical patient and the correct post-test probability. Secondary outcome measures included knowledge of diagnostic tests.Results917 participants attempted the survey and complete data were available from 874 (95.3%). Doctors randomized to the PMP and NFT arms were more likely to treat the patient than those randomized to the text-only arm. (ORs 1.49, 95% CI 1.02, 2.16) and 1.43, 95% CI 0.98, 2.08 respectively). More patients randomized to the PMP (87/218–39.9%) and NFT (73/207–35.3%) arms than the nomogram (50/194–25.8%) or text only (30/255–11.8%) arms reported the correct post-test probability (p

This dataset contains information about the 9761 witches from the Crypto Coven NFT project (https://www.cryptocoven.xyz/) collected using OpenSea API. The full data returned by the API is provided in witches_full.csv and a subset of the data is chosen by me and shared in witches.csv. The folder 'witch_images' includes the images of each witch in three different sizes.

I briefly describe the data in the witches.csv below:

• id: the id of the witch
• num_sales: number of sales in the past (till 4/21/2022 the day I collected the data)
• name: the name of the witch
• description: the description of the witch
• external_link: the link to the official page for the witch
• permalink: the OpenSea link for the witch
• token_metadata: the metadata JSON file about the witch
• token_id: the token_id of the NFT
• owner.user.username: the user name of the current owner
• owner.address: the wallet address of the current owner
• last_sale.total_price: the price of the last sale in gwei. Note that the unit here is gwei (giga and wei) and 1 ether = 1 billion gwei (18 zeros)
• last_sale.payment_token.usd_price: the USD price of 1 ether (ETH) for the last sale
• last_sale.transaction.timestamp: the timestamp of the last sale
• properties: there are 32 properties of each witch covering the different design elements of each witch, such as Skin Tone, Eyebrows, Body Shape, etc.

witches_full.csv is the full data provided by the OpenSea API, such as https://api.opensea.io/api/v1/asset/0x5180db8f5c931aae63c74266b211f580155ecac8/50. I just simply flattened the JSON returned by the API.

In January 2024, around ****** non-fungible tokens (NFTs) were sold in the art segment, generating approximately ********** U.S. dollars in sales. While slightly more than half of sales were profitable, there was still a financial loss of roughly *********** U.S. dollars.

In January 2024, around ******* non-fungible tokens (NFTs) were sold in the collectibles segment, generating about *** million U.S. dollars in sales. While slightly more than half of sales were profitable that month, there was still a financial loss of roughly ** million U.S. dollars.

Make money using NFT + AI | How to get started?

Introduction By now, you would have at least heard about NFT. If not, you would have heard about art which sold for $69 million. May be at-least record sales on NBA and with NFT. This got me curious, I started to research more on it recently by wearing my Data Scientist hat. Data Scientists follow when some numbers are there! In this article, I will share about NFT’s and how to get started.

MT Newswires’ team of highly experienced financial reporters produces timely and actionable commentary throughout the day to keep readers abreast of all the latest happenings in the digital marketplace: price spikes and price plunges in popular virtual coins, DeFi and NFT price action, regulatory updates, corporate adoption announcements, overarching industry trends, and more. Live Briefs Crypto News & Insights additionally incorporates educational “explainer” guides and longer form technical analysis to ensure that the content and crypto discovery is accessible to everyone – whether individual investors and traders entirely new to the concept or professional wealth managers looking for in-depth industry coverage to guide informed decision making on behalf of their clients. 

Every story includes relevant symbols and is category-coded to allow for seamless platform integration.

·       Top News – The most significant drivers of digital assets every day;  ·       Breaking News – real-time coverage of the events most likely to affect prices and adoption of cryptocurrencies and actively traded NFTs at any given moment; ·       Crypto Market Summaries – daily summaries covering major price action and regulatory developments globally; ·       Influencers & Social Buzz – objective coverage of the most talked about cryptocurrencies on social media and related sentiment indications; ·       Top Movers - intra-day updates on major price moves among the most popular cryptocurrencies; ·       Policy & Regulation - timely news on the rapidly evolving Digital Central Bank Currency policies with country specific regulatory developments; ·       Crypto Explainer - educational pieces to help investors understand the complex world of digital assets; ·       Get Digital - The Weekend Crypto Report, wrapping up the biggest digital currency news from the prior week and looking ahead to what may drive pricing in the week to come

Transactions in NFTs were significantly lower in 2024 than during the summer of 2021 when several tokens gained popularity. Most of these transactions were likely related to play-to-earn Vietnamese video game Axie Infinity, which became the world's most valuable NFT collection in August 2021 - although its sales volume did decline since. The gaming segment reported the highest sales volume of the non-fungible token (NFT) market in 2020, with over *** times the sales in sports projects. The overall market cap of NFTs in 2024, however, was noticeably smaller. NFT in 2024: Searching for legitimacy While cryptocurrency and Bitcoin saw their interest surge in early 2024 after the acceptance of Bitcoin ETFs in the United States, the NFT market has been struggling. For the larger audience, non-fungible tokens still seemed to be confusing what they are supposed to do, whereas crypto increasingly found legitimacy. The slowdown in the NFT market led one of the world's largest NFT marketplaces, OpenSea, to lay off large parts of its staff in October 2023. Solana to pave the way for NFTs? One of the blockchain networks that is closely affiliated with NFTs in 2024 is that of Solana. The monthly sales volume of this blockchain outperformed that of Ethereum in ************, causing Solana's market share in the overall crypto market to reach its highest value ever. Solana's position comes from relatively low costs but especially high transaction speeds and the sizable airdrops from multiple projects. This attracted significant amounts of capital, further fuelling the network. Solana's growth may provide the framework for the NFT market as a whole, as it slowly seeks to take over Ethereum's position in this part of the decentralized digital asset world.

Dataset Overview Each row represents a unique log emitted during transaction execution: • Canonical positioning: (block_number, tx_index, log_index) • Emitting contract address • Primary event topic (topic_zero) • Additional topics (data_topics) • Raw event data payload

All fields are stored exactly as produced by the node, with direct RLP verifiability for topics, data, and contract address.

Every log includes a deterministic _tracing_id that links the record to its genesis event and upstream transaction, forming the foundation for decoded events, swaps, liquidity, NFT events, and custom protocol decoders in downstream BlockDB products.

Chains and Coverage ETH, BSC, Base, Arbitrum, Unichain, Avalanche, Polygon, Celo, Linea, Optimism (others on request). Full history from chain genesis; reorg-aware real-time ingestion and updates.

Schema List of columns exactly as delivered: • block_number BIGINT – Block number that contains the emitting transaction • tx_index INTEGER – Zero-based index of the transaction within the block • log_index INTEGER – Zero-based position of the log within the transaction • contract_address BYTEA – 20-byte address of the contract that emitted the log • topic_zero BYTEA – 32-byte primary topic hash identifying the event type (NULL for anonymous events) • data_topics BYTEA[] – Array of additional topics (topics[1..n]), as raw bytes • data BYTEA – Raw event data payload as emitted on-chain • _tracing_id BYTEA – Deterministic lineage identifier of this log record • _created_at TIMESTAMPTZ – Record creation timestamp • _updated_at TIMESTAMPTZ – Record last update timestamp

Notes • Primary key: (block_number, tx_index, log_index) guarantees canonical ordering and uniqueness. • Foreign key: (block_number, tx_index) links each log directly to its canonical transaction record. • Indexes on contract_address, topic_zero, and (contract_address, topic_zero) support fast protocol- or event-specific scans. • Binary values can be rendered as hex via encode(column, 'hex') in SQL for display or downstream joins.

Lineage & Integrity Direct RLP-verifiable fields: contract_address, topic_zero, data_topics, data, and log_index are all directly or indirectly validated against node RLP.

_tracing_id provides a deterministic, cryptographic handle for each log row, enabling: • Provenance tracking from raw logs to decoded events and higher-level features • Reproducible analytics and signal extraction • Cross-system consistency checks (RPC vs. indexers vs. internal warehouses)

Common Use Cases • Building decoded event layers (swaps, LP actions, mints, burns, governance events, NFT activity) • Reconstructing DEX activity and liquidity flows directly from raw logs • Protocol-specific analytics (AMMs, lending, perpetuals, bridges, staking) from first principles • Detecting MEV patterns, liquidations, and arbitrage events at log-level resolution

Quality • Verifiable lineage: deterministic cryptographic hashes per row • Reorg-aware ingestion: continuity and consistency across forks • Complete historical coverage: from chain genesis to present